{"cells":[{"cell_type":"markdown","metadata":{},"source":["# 学生成绩（一元线性回归）"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":["import numpy as np\n","import pandas as pd\n","from sklearn.linear_model import  LinearRegression\n","from sklearn.metrics import  mean_squared_error,r2_score\n",""]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[{"output_type":"execute_result","data":{"text/plain":"     j3  s1  income\n0    76  82       2\n1    80  79       2\n2    64  72       2\n3    66  78       3\n4    89  82       2\n5    86  71       1\n6    75  74       2\n7    72  65       2\n8    42  60       3\n9    94  86       1\n10   80  83       2\n11   82  74       2\n12   58  61       2\n13   77  81       2\n14   75  71       2\n15   60  62       2\n16   57  63       2\n17   72  74       1\n18   77  70       1\n19   83  68       3\n20   81  80       2\n21   58  73       2\n22   83  82       2\n23   68  66       2\n24   50  42       1\n25   79  79       3\n26   83  93       3\n27   48  60       3\n28   81  92       2\n29   66  75       3\n30   84  74       2\n31   54  52       2\n32   63  79       2\n33   59  68       1\n34   88  85       3\n35  100  93       1\n36   71  70       2\n37   68  73       3\n38   45  45       1\n39   75  72       2\n40   78  78       2\n41   58  64       2\n42   98  84       1\n43   99  78       1\n44   70  80       3\n45   75  80       2\n46  100  99       3\n47   78  73       1\n48   97  93       2\n49   76  92       3","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>j3</th>\n      <th>s1</th>\n      <th>income</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>76</td>\n      <td>82</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>80</td>\n      <td>79</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>64</td>\n      <td>72</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>66</td>\n      <td>78</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>89</td>\n      <td>82</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>86</td>\n      <td>71</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>75</td>\n      <td>74</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>72</td>\n      <td>65</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>42</td>\n      <td>60</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>94</td>\n      <td>86</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>80</td>\n      <td>83</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>82</td>\n      <td>74</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>58</td>\n      <td>61</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>77</td>\n      <td>81</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>75</td>\n      <td>71</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>60</td>\n      <td>62</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>57</td>\n      <td>63</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>72</td>\n      <td>74</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>77</td>\n      <td>70</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>83</td>\n      <td>68</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>81</td>\n      <td>80</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>58</td>\n      <td>73</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>83</td>\n      <td>82</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>68</td>\n      <td>66</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>50</td>\n      <td>42</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>79</td>\n      <td>79</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>83</td>\n      <td>93</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>48</td>\n      <td>60</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>81</td>\n      <td>92</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>66</td>\n      <td>75</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>84</td>\n      <td>74</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>54</td>\n      <td>52</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>63</td>\n      <td>79</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>59</td>\n      <td>68</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>88</td>\n      <td>85</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>100</td>\n      <td>93</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>71</td>\n      <td>70</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>68</td>\n      <td>73</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>45</td>\n      <td>45</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>75</td>\n      <td>72</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>78</td>\n      <td>78</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>58</td>\n      <td>64</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>98</td>\n      <td>84</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>99</td>\n      <td>78</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>70</td>\n      <td>80</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>75</td>\n      <td>80</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>100</td>\n      <td>99</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>78</td>\n      <td>73</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>97</td>\n      <td>93</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>76</td>\n      <td>92</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{},"execution_count":2}],"source":["data=pd.read_csv(r\".\\highschool.txt\",sep=\" \")\n","data"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[],"source":["x=data['j3']\n","y=data['s1']\n","x=x.values.reshape(-1,1)"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"output_type":"execute_result","data":{"text/plain":"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"},"metadata":{},"execution_count":4}],"source":["linear=LinearRegression()\n","linear.fit(x,y)"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[{"output_type":"stream","name":"stdout","text":"回归方程系数： [0.65110749]\n"}],"source":["print(\"回归方程系数：\",linear.coef_)"]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[{"output_type":"stream","name":"stdout","text":"回归方程截距 26.444090110945766\n"}],"source":["print(\"回归方程截距\",linear.intercept_)"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"output_type":"execute_result","data":{"text/plain":"0.6324825640920154"},"metadata":{},"execution_count":7}],"source":["linear.score(x,y)"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[],"source":["y_pred=linear.predict(x)"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[{"output_type":"stream","name":"stdout","text":"mean_squared_error: 50.055874770667494\nr2_score: 0.6324825640920154\n"}],"source":["print(\"mean_squared_error:\",mean_squared_error(y_true=y,y_pred=y_pred))\n","print(\"r2_score:\",r2_score(y_true=y,y_pred=y_pred))"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[],"source":["x_min=x.min()\n","x_max=x.max()\n","x_index=np.arange(x_min,x_max).reshape(-1,1)"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[],"source":["import matplotlib.pyplot as plt\n","plt.rcParams['font.sans-serif']='SimHei'"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[{"output_type":"execute_result","data":{"text/plain":"Text(0.5, 1.0, '50名同学初三成绩和高三成绩回归分析')"},"metadata":{},"execution_count":12},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/svg+xml":"<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"275.894687pt\" version=\"1.1\" viewBox=\"0 0 384.379687 275.894687\" width=\"384.379687pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <defs>\r\n  <style type=\"text/css\">\r\n*{stroke-linecap:butt;stroke-linejoin:round;}\r\n  </style>\r\n </defs>\r\n <g id=\"figure_1\">\r\n  <g id=\"patch_1\">\r\n   <path d=\"M 0 275.894687 \r\nL 384.379687 275.894687 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波士顿房价（多元线性回归）"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[],"source":["from sklearn.datasets import load_boston"]},{"cell_type":"code","execution_count":14,"metadata":{},"outputs":[],"source":["data=load_boston()\n","x=data['data']\n","y=data['target']"]},{"cell_type":"code","execution_count":15,"metadata":{},"outputs":[{"output_type":"execute_result","data":{"text/plain":"array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n       'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7')"},"metadata":{},"execution_count":15}],"source":["data['feature_names']"]},{"cell_type":"code","execution_count":16,"metadata":{},"outputs":[],"source":["from sklearn.model_selection import train_test_split"]},{"cell_type":"code","execution_count":17,"metadata":{},"outputs":[],"source":["x_train,x_test,y__train,y_test=train_test_split(x,y,test_size=0.2)"]},{"cell_type":"code","execution_count":18,"metadata":{},"outputs":[{"output_type":"execute_result","data":{"text/plain":"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"},"metadata":{},"execution_count":18}],"source":["model=LinearRegression()\n","model.fit(x_train,y__train)"]},{"cell_type":"code","execution_count":19,"metadata":{},"outputs":[{"output_type":"stream","name":"stdout","text":"回归方程系数： [-1.06211209e-01  4.31159085e-02  1.49782066e-02  2.90996975e+00\n -1.81447175e+01  3.55736218e+00  7.23283097e-03 -1.46082800e+00\n  3.54483764e-01 -1.40140508e-02 -9.44843921e-01  9.13132655e-03\n -5.55834426e-01]\n回归方程截距 38.59863936995489\n"}],"source":["print(\"回归方程系数：\",model.coef_)\n","print(\"回归方程截距\",model.intercept_)"]},{"cell_type":"code","execution_count":20,"metadata":{},"outputs":[],"source":["y_pred=model.predict(x_test)"]},{"cell_type":"code","execution_count":21,"metadata":{},"outputs":[{"output_type":"stream","name":"stdout","text":"mean_squared_error: 13.763203242515617\nr2_score: 0.8049297977728928\n"}],"source":["print(\"mean_squared_error:\",mean_squared_error(y_true=y_test,y_pred=y_pred))\n","print(\"r2_score:\",r2_score(y_true=y_test,y_pred=y_pred))"]},{"cell_type":"code","execution_count":22,"metadata":{},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/svg+xml":"<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"262.715pt\" 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\n"},"metadata":{"needs_background":"light"}}],"source":["plt.plot(range(len(y_test)),y_test)\n","plt.plot(range(len(y_pred)),y_pred)\n","plt.legend(['real','predict'])\n","plt.ylabel('房价')\n","plt.title('房价真实值与预测值对比')\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["# 学生成绩（一元多项式回归）"]},{"cell_type":"code","execution_count":24,"metadata":{},"outputs":[{"output_type":"execute_result","data":{"text/plain":"     j3  s1  income\n0    76  82       2\n1    80  79       2\n2    64  72       2\n3    66  78       3\n4    89  82       2\n5    86  71       1\n6    75  74       2\n7    72  65       2\n8    42  60       3\n9    94  86       1\n10   80  83       2\n11   82  74       2\n12   58  61       2\n13   77  81       2\n14   75  71       2\n15   60  62       2\n16   57  63       2\n17   72  74       1\n18   77  70       1\n19   83  68       3\n20   81  80       2\n21   58  73       2\n22   83  82       2\n23   68  66       2\n24   50  42       1\n25   79  79       3\n26   83  93       3\n27   48  60       3\n28   81  92       2\n29   66  75       3\n30   84  74       2\n31   54  52       2\n32   63  79       2\n33   59  68       1\n34   88  85       3\n35  100  93       1\n36   71  70       2\n37   68  73       3\n38   45  45       1\n39   75  72       2\n40   78  78       2\n41   58  64       2\n42   98  84       1\n43   99  78       1\n44   70  80       3\n45   75  80       2\n46  100  99       3\n47   78  73       1\n48   97  93       2\n49   76  92       3","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>j3</th>\n      <th>s1</th>\n      <th>income</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>76</td>\n      <td>82</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>80</td>\n      <td>79</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>64</td>\n      <td>72</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>66</td>\n      <td>78</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>89</td>\n      <td>82</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>86</td>\n      <td>71</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>75</td>\n      <td>74</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>72</td>\n      <td>65</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>42</td>\n      <td>60</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>94</td>\n      <td>86</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>80</td>\n      <td>83</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>82</td>\n      <td>74</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>58</td>\n      <td>61</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>77</td>\n      <td>81</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>75</td>\n      <td>71</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>60</td>\n      <td>62</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>57</td>\n      <td>63</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>72</td>\n      <td>74</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>77</td>\n      <td>70</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>83</td>\n      <td>68</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>81</td>\n      <td>80</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>58</td>\n      <td>73</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>83</td>\n      <td>82</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>68</td>\n      <td>66</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>50</td>\n      <td>42</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>79</td>\n      <td>79</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>83</td>\n      <td>93</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>48</td>\n      <td>60</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>81</td>\n      <td>92</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>66</td>\n      <td>75</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>84</td>\n      <td>74</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>54</td>\n      <td>52</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>63</td>\n      <td>79</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>59</td>\n      <td>68</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>88</td>\n      <td>85</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>100</td>\n      <td>93</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>71</td>\n      <td>70</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>68</td>\n      <td>73</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>45</td>\n      <td>45</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>75</td>\n      <td>72</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>78</td>\n      <td>78</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>58</td>\n      <td>64</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>98</td>\n      <td>84</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>99</td>\n      <td>78</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>70</td>\n      <td>80</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>75</td>\n      <td>80</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>100</td>\n      <td>99</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>78</td>\n      <td>73</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>97</td>\n      <td>93</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>76</td>\n      <td>92</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{},"execution_count":24}],"source":["data=pd.read_csv(r\".\\highschool.txt\",sep=\" \")\n","data"]},{"cell_type":"code","execution_count":26,"metadata":{},"outputs":[],"source":["x=data['j3'].values.reshape(-1,1)\n","y=data['s1']"]},{"cell_type":"code","execution_count":27,"metadata":{},"outputs":[],"source":["from sklearn.preprocessing import PolynomialFeatures"]},{"cell_type":"code","execution_count":29,"metadata":{},"outputs":[],"source":["poly_reg=PolynomialFeatures(degree=2)\n","x_poly=poly_reg.fit_transform(x)"]},{"cell_type":"code","execution_count":31,"metadata":{},"outputs":[{"output_type":"execute_result","data":{"text/plain":"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"},"metadata":{},"execution_count":31}],"source":["linear=LinearRegression()\n","linear.fit(x_poly,y)"]},{"cell_type":"code","execution_count":32,"metadata":{},"outputs":[{"output_type":"execute_result","data":{"text/plain":"array([ 0.        ,  1.43902662, 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\n"},"metadata":{"needs_background":"light"}}],"source":["plt.scatter(x,y,color='red')\n","\n","x_min=x.min()\n","x_max=x.max()\n","x_index=np.arange(x_min,x_max).reshape(-1,1)\n","\n","plt.plot(x_index,linear.predict(poly_reg.fit_transform(x_index)),color='blue')\n","plt.xlabel(\"初三成绩\")\n","plt.ylabel('高一成绩')\n","plt.title(\"学生成绩医院多项式回归\")"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]}],"metadata":{"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.7-final"},"orig_nbformat":2,"kernelspec":{"name":"python3","display_name":"Python 3"}},"nbformat":4,"nbformat_minor":2}